Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

2018-02-22 Thread Michael Sumner
Some thoughts.


On Wed, 21 Feb 2018 at 09:09 Li Jin <jin...@ga.gov.au> wrote:

> The statement ‘the kriging functions in R still don't accept lat/long’ is
> incorrect. Please check the gstat and spm packages for details. When your
> data is collected from one utm, it is a good idea to project the data using
> utm. If the data is from two or more utms, you need to use different
> projection systems. The references provided demonstrated that the commonly
> used WGS84 is as good as relevant projection systems.
>
> From: Dominik Schneider [mailto:dominik.schnei...@colorado.edu]
> Sent: Wednesday, 21 February 2018 5:02 AM
> To: Li Jin
> Cc: Stefano Sofia; r-sig-geo@r-project.org
> Subject: Re: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a
> Digital Elevation Model [SEC=UNCLASSIFIED]
>
> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used.
> Fair enough, thanks for sending the references. But, as far as I know, the
> kriging functions in R still don't accept lat/long.
>
>
>

Any such advice is completely dependent on the study area, and the goals of
the study. UTM is really bad advice generally, it's just a simplistic
system we've inherited and is used way too much, a self-fulfilling
prophecy. Whether standard tools should or shouldn't accept data as given
is a crux philosophical point, no tool in R is smart enough to know whether
it's "correct enough" to assume one way or another. You can't assume any
measurement represents reality in any projection, it depends how far, how
much, how large - you can't traverse from local neighbourhood scales to
continental, for example - you'd make different choices regarding
compromises at *some such point*.

Please don't ever advise use of UTM without specific caveats about the
scope and extent of the research - which is impossible in general - learn
to use map projections with the compromises they entail, there's nothing
stopping creating a local new one, from any of the main families with
PROJ.4, and with many variants of compromises on area, length, shape and
scale.

I tend not to say anything about this topic in this environment, but this
time the back and forth is particularly misleading IMO.

We actually have the worst of worlds at the moment, with many softwares
opinionatedly preventing one from making educational mistakes. There's no
real authority, lots of opinion and habit. lots of exploration but not
enough pushing and argument - I advise keeping an open mind and exploring
deeply.

Cheers, Mike.

>
> On Mon, Feb 19, 2018 at 8:54 PM, Li Jin <jin...@ga.gov.au jin...@ga.gov.au>> wrote:
> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used. Please see the following studies
> for details.
>
> Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to
> Spatial Reference Systems for Predicting Marine Environmental Variables,
> 20th International Congress on Modelling and Simulation: Adelaide,
> Australia, pp. 387-393.
> Jiang, W., Li, J., 2014. The effects of spatial reference systems on the
> predictive accuracy of spatial interpolation methods. Record 2014/01.
> Geoscience Australia: Canberra, pp 33.
> http://dx.doi.org/10.11636/Record.2014.001.
> Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference
> Systems on the Accuracy of Spatial Predictive Modelling along a Latitudinal
> Gradient, 22nd International Congress on Modelling and Simulation: Hobart,
> Tasmania, Australia, pp. 106-112.
>
>
> -Original Message-
> From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org r-sig-geo-boun...@r-project.org>] On Behalf Of Dominik Schneider
> Sent: Wednesday, 14 February 2018 3:21 AM
> To: Stefano Sofia
> Cc: r-sig-geo@r-project.org<mailto:r-sig-geo@r-project.org>
> Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital
> Elevation Model
>
> You can't use a lat/long coordinate system when kriging because the
> concept of distance is ambiguous. Convert all your data a UTM grid like you
> had in your first post and it should work.
>
> Another note, It looks like you are working at 0.01 deg which is on the
> order of 1km resolution so you may find  other covariates such as aspect,
> slope, and wind sheltering/exposure, terrain roughness for estimating snow
> on the ground useful. see some of the earliest papers by Carroll, Cressie,
> and Elder.
>
> Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical
> methodologies used to estimate snow water equivalent, *JAWRA Journal of the
> American Water Resources Association*, *32*(2), 267–278,
> doi:10./j.1752-1688.1996.tb03450.x.
>
> Carroll, S. S., and N. Cressie (1997), Spatial modeling of sn

Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

2018-02-20 Thread Li Jin
The statement ‘the kriging functions in R still don't accept lat/long’ is 
incorrect. Please check the gstat and spm packages for details. When your data 
is collected from one utm, it is a good idea to project the data using utm. If 
the data is from two or more utms, you need to use different projection 
systems. The references provided demonstrated that the commonly used WGS84 is 
as good as relevant projection systems.

From: Dominik Schneider [mailto:dominik.schnei...@colorado.edu]
Sent: Wednesday, 21 February 2018 5:02 AM
To: Li Jin
Cc: Stefano Sofia; r-sig-geo@r-project.org
Subject: Re: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital 
Elevation Model [SEC=UNCLASSIFIED]

The effects of spatial reference systems on interpolations and accuracy are 
minimal, and lat and long can be used.
Fair enough, thanks for sending the references. But, as far as I know, the 
kriging functions in R still don't accept lat/long.



On Mon, Feb 19, 2018 at 8:54 PM, Li Jin 
<jin...@ga.gov.au<mailto:jin...@ga.gov.au>> wrote:
The effects of spatial reference systems on interpolations and accuracy are 
minimal, and lat and long can be used. Please see the following studies for 
details.

Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to Spatial 
Reference Systems for Predicting Marine Environmental Variables, 20th 
International Congress on Modelling and Simulation: Adelaide, Australia, pp. 
387-393.
Jiang, W., Li, J., 2014. The effects of spatial reference systems on the 
predictive accuracy of spatial interpolation methods. Record 2014/01. 
Geoscience Australia: Canberra, pp 33. 
http://dx.doi.org/10.11636/Record.2014.001.
Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference Systems on 
the Accuracy of Spatial Predictive Modelling along a Latitudinal Gradient, 22nd 
International Congress on Modelling and Simulation: Hobart, Tasmania, 
Australia, pp. 106-112.


-Original Message-
From: R-sig-Geo 
[mailto:r-sig-geo-boun...@r-project.org<mailto:r-sig-geo-boun...@r-project.org>]
 On Behalf Of Dominik Schneider
Sent: Wednesday, 14 February 2018 3:21 AM
To: Stefano Sofia
Cc: r-sig-geo@r-project.org<mailto:r-sig-geo@r-project.org>
Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital 
Elevation Model

You can't use a lat/long coordinate system when kriging because the concept of 
distance is ambiguous. Convert all your data a UTM grid like you had in your 
first post and it should work.

Another note, It looks like you are working at 0.01 deg which is on the order 
of 1km resolution so you may find  other covariates such as aspect, slope, and 
wind sheltering/exposure, terrain roughness for estimating snow on the ground 
useful. see some of the earliest papers by Carroll, Cressie, and Elder.

Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical 
methodologies used to estimate snow water equivalent, *JAWRA Journal of the 
American Water Resources Association*, *32*(2), 267–278, 
doi:10./j.1752-1688.1996.tb03450.x.

Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water 
equivalent using covariances estimated from spatial and geomorphic attributes, 
*Journal of Hydrology*, *190*(1-2), 42–59.

Balk, B., and K. Elder (2000), Combining binary decision tree and 
geostatistical methods to estimate snow distribution in a mountain watershed, 
*Water Resources Research*, *36*(1), 13–26, doi:10.1029/1999WR900251.

Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial 
interpolation methods for estimating snow distribution in the Colorado Rocky 
Mountains, *Hydrological Processes*, *16*(18), 3627–3649, doi:10.1002/hyp.1239.

Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of 
topographic controls on the spatial distribution of snow in rugged mountain 
terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014, 
doi:10.1029/2003WR002973.


On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia < 
stefano.so...@regione.marche.it<mailto:stefano.so...@regione.marche.it>> wrote:

> Dear Daniel and list users,
> I tried to follow the instructions but I encountered two kinds of errors.
> This is a reproducibile code:
>
> 
> ---
> library(automap)
> library(ggplot2)
> library(gstat)
> library(raster)
> library(rasterVis)
> library(rgdal)
> library(maptools)
>
> ## LOADING DEM
> ita_DEM <- getData('alt', country='ITA', mask=TRUE)
> crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> +ellps=WGS84 +towgs84=0,0,0"
> #ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
> str(ita_DEM)
>
> ## LOADING RAINFALL DATA
> rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4),
> Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.2815

Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

2018-02-20 Thread Dominik Schneider
>
> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used.

Fair enough, thanks for sending the references. But, as far as I know, the
kriging functions in R still don't accept lat/long.



On Mon, Feb 19, 2018 at 8:54 PM, Li Jin  wrote:

> The effects of spatial reference systems on interpolations and accuracy
> are minimal, and lat and long can be used. Please see the following studies
> for details.
>
> Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to
> Spatial Reference Systems for Predicting Marine Environmental Variables,
> 20th International Congress on Modelling and Simulation: Adelaide,
> Australia, pp. 387-393.
> Jiang, W., Li, J., 2014. The effects of spatial reference systems on the
> predictive accuracy of spatial interpolation methods. Record 2014/01.
> Geoscience Australia: Canberra, pp 33. http://dx.doi.org/10.11636/
> Record.2014.001.
> Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference
> Systems on the Accuracy of Spatial Predictive Modelling along a Latitudinal
> Gradient, 22nd International Congress on Modelling and Simulation: Hobart,
> Tasmania, Australia, pp. 106-112.
>
>
> -Original Message-
> From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of
> Dominik Schneider
> Sent: Wednesday, 14 February 2018 3:21 AM
> To: Stefano Sofia
> Cc: r-sig-geo@r-project.org
> Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital
> Elevation Model
>
> You can't use a lat/long coordinate system when kriging because the
> concept of distance is ambiguous. Convert all your data a UTM grid like you
> had in your first post and it should work.
>
> Another note, It looks like you are working at 0.01 deg which is on the
> order of 1km resolution so you may find  other covariates such as aspect,
> slope, and wind sheltering/exposure, terrain roughness for estimating snow
> on the ground useful. see some of the earliest papers by Carroll, Cressie,
> and Elder.
>
> Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical
> methodologies used to estimate snow water equivalent, *JAWRA Journal of the
> American Water Resources Association*, *32*(2), 267–278,
> doi:10./j.1752-1688.1996.tb03450.x.
>
> Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water
> equivalent using covariances estimated from spatial and geomorphic
> attributes, *Journal of Hydrology*, *190*(1-2), 42–59.
>
> Balk, B., and K. Elder (2000), Combining binary decision tree and
> geostatistical methods to estimate snow distribution in a mountain
> watershed, *Water Resources Research*, *36*(1), 13–26,
> doi:10.1029/1999WR900251.
>
> Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial
> interpolation methods for estimating snow distribution in the Colorado
> Rocky Mountains, *Hydrological Processes*, *16*(18), 3627–3649,
> doi:10.1002/hyp.1239.
>
> Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of
> topographic controls on the spatial distribution of snow in rugged mountain
> terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014,
> doi:10.1029/2003WR002973.
>
>
> On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia <
> stefano.so...@regione.marche.it> wrote:
>
> > Dear Daniel and list users,
> > I tried to follow the instructions but I encountered two kinds of errors.
> > This is a reproducibile code:
> >
> > 
> > ---
> > library(automap)
> > library(ggplot2)
> > library(gstat)
> > library(raster)
> > library(rasterVis)
> > library(rgdal)
> > library(maptools)
> >
> > ## LOADING DEM
> > ita_DEM <- getData('alt', country='ITA', mask=TRUE)
> > crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> > +ellps=WGS84 +towgs84=0,0,0"
> > #ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
> > str(ita_DEM)
> >
> > ## LOADING RAINFALL DATA
> > rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4),
> > Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.28157),
> > Lat_Cent=c(43.79447, 43.85185, 43.76267, 43.03470, 43.08003),
> > Altitude=c(112.20, 42.93, 36.14, 747, 465))
> >
> > stations <- data.frame(rain_data$Long_Cent, rain_data$Lat_Cent)
> > rain_data <- SpatialPointsDataFrame(stations, rain_data,
> > proj4string=CRS("+init=epsg:4326"))
> > stations <- SpatialPoints(stations,
> > proj4string=CRS("+init=epsg:4326"))
> >
> > ## EXTRACT THE ELEVATION VALUES TO MY POINTS
> > rain_data$ExtractedElevationValues <- extract(x=ita_DEM, y=stations)
> >
> > ## CREATE GRID FOR KRIGING OUTPUT
> > minx <-  rain_data@bbox[1,1]
> > maxx <- rain_data@bbox[1,2]
> > miny <- rain_data@bbox[2,1]
> > maxy <- rain_data@bbox[2,2]
> > pixel <- 0.01
> > grd <- expand.grid(x=seq(minx, maxx, by=pixel), y=seq(miny, maxy,
> > by=pixel))
> > coordinates(grd) <- ~x+y
> > gridded(grd) <- TRUE
> > proj4string(grd) <- 

Re: [R-sig-Geo] [DKIM] Re: Interpolating snowfall values on a Digital Elevation Model [SEC=UNCLASSIFIED]

2018-02-19 Thread Li Jin
The effects of spatial reference systems on interpolations and accuracy are 
minimal, and lat and long can be used. Please see the following studies for 
details.

Jiang, W., Li, J., 2013. Are Spatial Modelling Methods Sensitive to Spatial 
Reference Systems for Predicting Marine Environmental Variables, 20th 
International Congress on Modelling and Simulation: Adelaide, Australia, pp. 
387-393.
Jiang, W., Li, J., 2014. The effects of spatial reference systems on the 
predictive accuracy of spatial interpolation methods. Record 2014/01. 
Geoscience Australia: Canberra, pp 33. 
http://dx.doi.org/10.11636/Record.2014.001.
Turner, A.J., Li, J., Jiang, W., 2017. Effects of Spatial Reference Systems on 
the Accuracy of Spatial Predictive Modelling along a Latitudinal Gradient, 22nd 
International Congress on Modelling and Simulation: Hobart, Tasmania, 
Australia, pp. 106-112.


-Original Message-
From: R-sig-Geo [mailto:r-sig-geo-boun...@r-project.org] On Behalf Of Dominik 
Schneider
Sent: Wednesday, 14 February 2018 3:21 AM
To: Stefano Sofia
Cc: r-sig-geo@r-project.org
Subject: [DKIM] Re: [R-sig-Geo] Interpolating snowfall values on a Digital 
Elevation Model

You can't use a lat/long coordinate system when kriging because the concept of 
distance is ambiguous. Convert all your data a UTM grid like you had in your 
first post and it should work.

Another note, It looks like you are working at 0.01 deg which is on the order 
of 1km resolution so you may find  other covariates such as aspect, slope, and 
wind sheltering/exposure, terrain roughness for estimating snow on the ground 
useful. see some of the earliest papers by Carroll, Cressie, and Elder.

Carroll, S. S., and N. Cressie (1996), A comparison of geostatistical 
methodologies used to estimate snow water equivalent, *JAWRA Journal of the 
American Water Resources Association*, *32*(2), 267–278, 
doi:10./j.1752-1688.1996.tb03450.x.

Carroll, S. S., and N. Cressie (1997), Spatial modeling of snow water 
equivalent using covariances estimated from spatial and geomorphic attributes, 
*Journal of Hydrology*, *190*(1-2), 42–59.

Balk, B., and K. Elder (2000), Combining binary decision tree and 
geostatistical methods to estimate snow distribution in a mountain watershed, 
*Water Resources Research*, *36*(1), 13–26, doi:10.1029/1999WR900251.

Erxleben, J., K. Elder, and R. Davis (2002), Comparison of spatial 
interpolation methods for estimating snow distribution in the Colorado Rocky 
Mountains, *Hydrological Processes*, *16*(18), 3627–3649, doi:10.1002/hyp.1239.

Erickson, T. A., M. W. Williams, and A. Winstral (2005), Persistence of 
topographic controls on the spatial distribution of snow in rugged mountain 
terrain, Colorado, United States, *Water Resour. Res.*, *41*(4), W04014, 
doi:10.1029/2003WR002973.


On Tue, Feb 13, 2018 at 3:45 AM, Stefano Sofia < 
stefano.so...@regione.marche.it> wrote:

> Dear Daniel and list users,
> I tried to follow the instructions but I encountered two kinds of errors.
> This is a reproducibile code:
>
> 
> ---
> library(automap)
> library(ggplot2)
> library(gstat)
> library(raster)
> library(rasterVis)
> library(rgdal)
> library(maptools)
>
> ## LOADING DEM
> ita_DEM <- getData('alt', country='ITA', mask=TRUE)
> crs(ita_DEM) <- "+init=epsg:4326 +proj=longlat +datum=WGS84 +no_defs
> +ellps=WGS84 +towgs84=0,0,0"
> #ita_DEM <- as(ita_DEM, "SpatialGridDataFrame")
> str(ita_DEM)
>
> ## LOADING RAINFALL DATA
> rain_data <- data.frame(Cumulata=c(11.8, 9.0, 8.0, 36.6, 9.4), 
> Long_Cent=c(12.61874, 12.78690, 12.96756, 13.15599, 13.28157), 
> Lat_Cent=c(43.79447, 43.85185, 43.76267, 43.03470, 43.08003), 
> Altitude=c(112.20, 42.93, 36.14, 747, 465))
>
> stations <- data.frame(rain_data$Long_Cent, rain_data$Lat_Cent) 
> rain_data <- SpatialPointsDataFrame(stations, rain_data,
> proj4string=CRS("+init=epsg:4326"))
> stations <- SpatialPoints(stations, 
> proj4string=CRS("+init=epsg:4326"))
>
> ## EXTRACT THE ELEVATION VALUES TO MY POINTS 
> rain_data$ExtractedElevationValues <- extract(x=ita_DEM, y=stations)
>
> ## CREATE GRID FOR KRIGING OUTPUT
> minx <-  rain_data@bbox[1,1]
> maxx <- rain_data@bbox[1,2]
> miny <- rain_data@bbox[2,1]
> maxy <- rain_data@bbox[2,2]
> pixel <- 0.01
> grd <- expand.grid(x=seq(minx, maxx, by=pixel), y=seq(miny, maxy,
> by=pixel))
> coordinates(grd) <- ~x+y
> gridded(grd) <- TRUE
> proj4string(grd) <- CRS("+init=epsg:4326")
>
> ## KRIGING: autoKrige(YourMeasurements ~ YourExtractedElevationValues, 
> YourMeasurementLocations, TargetGrid) OK_snow <- autoKrige(Cumulata ~ 
> rain_data$ExtractedElevationValues,
> rain_data, grd)
> 
> ---
>
> The error I get is:
> Error in autoKrige(Cumulata ~ rain_data$ExtractedElevationValues,
> rain_data,  :
>   Either input_data or